Fig. 1

NA

Fig. 2

Fig. 3

Fig. 4

# Libraries needed to plot absorbance data 
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(zoo)
library(scico)
library(plotly)


# Data
aps.master <- readRDS(file="Data/APSmaster.rds")
aps.master$loc <- as.factor(aps.master$loc)
abs350_t <- readRDS(file="Data/abs350_t.rds")

colores <- scico(7,palette="romaO",begin = 0.1,
                 end = 0.9) #Color assignment

# Plotting
v <- 
  aps.master %>%
  mutate(loc = fct_relevel(loc, 
                           "FOR","SNO", "GLO","GRA","PLA","SKY","MUS")) %>%
  ggplot(aes(x=loc,y=sloperatio)) +geom_jitter(aes(color=loc),width=0.1,alpha=0.5) + theme_classic2(base_size=9) + stat_boxplot(outlier.shape=NA,fill=NA) +
  labs(x="Sampling location",y="Slope ratio",color="Location")+
  scale_color_scico_d(palette="romaO",begin = 0.1,
                      end = 0.9)+
  theme(axis.text.x=element_text(angle=60, hjust=1),legend.position="none")

a <- 
  abs350_t %>% group_by(loc) %>% filter(loc %in% c("FOR","SNO", "GLO","GRA","PLA","SKY","MUS")) %>%
  summarise_all(median,na.rm=TRUE) %>% mutate(loc = fct_relevel(loc, 
                                                              "FOR","SNO", "GLO","GRA","PLA","SKY","MUS"))  %>% 
  gather("param","value",-loc) %>% 
  ggplot(aes(x=as.numeric(param),y=as.numeric(value),color=loc)) +
  geom_smooth(se=FALSE,na.rm=TRUE,span=0.1,alpha=0.25,linewidth=0.5) + 
  coord_cartesian(xlim = c(200,500),
                  ylim = c(0,40)) + theme_classic2(base_size = 9) +
  labs(x="Wavelength (nm)",y="Absorption coefficient [m^-1]",color="Location")+
  scale_color_scico_d(palette="romaO",begin = 0.1,
                      end = 0.9) + theme(legend.position = c(0.9, 0.55),
                                         legend.title=element_text(size=8), 
                                         legend.text=element_text(size=7),
                                         #legend.background = element_rect(fill = "white", color = "black")
                                         ) + scale_y_continuous(trans='sqrt')

subplot(a,v,nrows=2) #alternative just for markdown
#a/v + plot_annotation(tag_levels = 'A') 
#ggsave(filename="Endres_Fig4.tiff",height=160*1.3,width=80*1.3,units='mm',dpi=600)

Fig. 5

Spatial variability of Fluorescence in LaVallina. EEMs have been pre-processed with the library eemR.

### Figure Spatial Temporal variances
## A: AVG and SD arrays
## B: Stacked Barplot

library(tidyverse)
library(ggplot2)
library(stringr)
library(eemR)
library(scico)


## Load EEMs
FOR_eem <- readRDS(file="Data/EEM/FOR_eem.rds")
SNO_eem <- readRDS(file="Data/EEM/SNO_eem.rds")
GLO_eem <- readRDS(file="Data/EEM/GLO_eem.rds")
GRA_eem <- readRDS(file="Data/EEM/GRA_eem.rds")
PLA_eem <- readRDS(file="Data/EEM/PLA_eem.rds")
SKY_eem <- readRDS(file="Data/EEM/SKY_eem.rds")
MUS_eem <- readRDS(file="Data/EEM/MUS_eem.rds")

#Calculate AVG and SD matrices
my.list <- list()
for (i in 1:length(FOR_eem)){my.list[[i]]<-FOR_eem[[i]]$x}
FOR_avg <- apply(simplify2array(my.list), 1:2, mean)
FOR_sd <- apply(simplify2array(my.list), 1:2, sd)

my.list <- list()
for (i in 1:length(SNO_eem)){my.list[[i]]<-SNO_eem[[i]]$x}
SNO_avg <- apply(simplify2array(my.list), 1:2, mean)
SNO_sd <- apply(simplify2array(my.list), 1:2, sd)

my.list <- list()
for (i in 1:length(GLO_eem)){my.list[[i]]<-GLO_eem[[i]]$x}
GLO_avg <- apply(simplify2array(my.list), 1:2, mean)
GLO_sd <- apply(simplify2array(my.list), 1:2, sd)

my.list <- list()
for (i in 1:length(GRA_eem)){my.list[[i]]<-GRA_eem[[i]]$x}
GRA_avg <- apply(simplify2array(my.list), 1:2, mean)
GRA_sd <- apply(simplify2array(my.list), 1:2, sd)

my.list <- list()
for (i in 1:length(SKY_eem)){my.list[[i]]<-SKY_eem[[i]]$x}
SKY_avg <- apply(simplify2array(my.list), 1:2, mean)
SKY_sd <- apply(simplify2array(my.list), 1:2, sd)

my.list <- list()
for (i in 1:length(PLA_eem)){my.list[[i]]<-PLA_eem[[i]]$x}
PLA_avg <- apply(simplify2array(my.list), 1:2, mean)
PLA_sd <- apply(simplify2array(my.list), 1:2, sd)

my.list <- list()
for (i in 1:length(MUS_eem)){my.list[[i]]<-MUS_eem[[i]]$x}
MUS_avg <- apply(simplify2array(my.list), 1:2, mean)
MUS_sd <- apply(simplify2array(my.list), 1:2, sd)


#get colors as discrete values
colores <- scico(7,palette="romaO",begin = 0.1,
                 end = 0.9)
## Calculate the plots
###Calculate AVG ggplots (Warning messages are because Ex/Em wavelength are cut with ylim)
M1 <- matrix(FOR_avg,nrow=length(FOR_eem[[i]]$em),dimnames=list(FOR_eem[[i]]$em,FOR_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p1 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank(),plot.title = element_text(hjust = 0.5)) + ylim(250,500) + coord_flip() + labs(subtitle="Mean EEM") + 
  xlab(expression(atop(bold("FOR (9)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow') +
  #scale_fill_batlow_d(drop = FALSE) +
  theme_minimal(base_size=10) + theme(plot.subtitle=element_text(hjust = 0.5),axis.title.y = element_text(colour=colores[1]))
#p1
#ggsave("04_Output/Plot/EEM/FOR.png")

M1 <- matrix(SNO_avg,nrow=length(SNO_eem[[i]]$em),dimnames=list(SNO_eem[[i]]$em,SNO_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p2 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) + coord_flip() + #labs(title="SNO") +
  xlab(expression(atop(bold("SNO (3)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow') +theme_minimal(base_size=10) +
  theme(axis.title.y = element_text(colour=colores[2]))
#p
# p2
# ggsave("04_Output/Plot/EEM/SNO.png")

M1 <- matrix(GLO_avg,nrow=length(GLO_eem[[i]]$em),dimnames=list(GLO_eem[[i]]$em,GLO_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p3 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +  coord_flip() + #labs(title="GLO") +
  xlab(expression(atop(bold("GLO (13)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow',name = "Mean level (R.U.)") +theme_minimal(base_size=10)+
  theme(axis.title.y = element_text(colour=colores[3]))
# p3
# ggsave("04_Output/Plot/EEM/GLO.png")

M1 <- matrix(GRA_avg,nrow=length(GRA_eem[[i]]$em),dimnames=list(GRA_eem[[i]]$em,GRA_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p4 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +  coord_flip() + #labs(title="GRA") +
  xlab(expression(atop(bold("GRA (11)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow') +theme_minimal(base_size=10)+
  theme(axis.title.y = element_text(colour=colores[4]))
# p4
# ggsave("04_Output/Plot/EEM/GRA.png")

M1 <- matrix(SKY_avg,nrow=length(SKY_eem[[i]]$em),dimnames=list(SKY_eem[[i]]$em,SKY_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p5 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +  coord_flip() + #labs(title="SKY") +
  xlab(expression(atop(bold("SKY (13)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow') +theme_minimal(base_size=10)+
  theme(axis.title.y = element_text(colour=colores[6]))
# p5
# ggsave("04_Output/Plot/EEM/SKY.png")

M1 <- matrix(PLA_avg,nrow=length(PLA_eem[[i]]$em),dimnames=list(PLA_eem[[i]]$em,PLA_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p6 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +  coord_flip() + #labs(title="PLA") +
  xlab(expression(atop(bold("PLA (13)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow') +theme_minimal(base_size=10)+
  theme(axis.title.y = element_text(colour=colores[5]))
# p6
# ggsave("04_Output/Plot/EEM/PLA.png")

M1 <- matrix(MUS_avg,nrow=length(MUS_eem[[i]]$em),dimnames=list(MUS_eem[[i]]$em,MUS_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
p7 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.015,0.02,0.025,0.03,0.04,0.05,0.075,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +  coord_flip() + #labs(title="MUS") +
  xlab(expression(atop(bold("MUS (3)"), paste("Em"))))+
  scale_fill_scico_d(drop = FALSE,palette='batlow') +theme_minimal(base_size=10)+
  theme(axis.title.y = element_text(colour=colores[7]),axis.text.x=element_text(angle=60, hjust=1))
# p7
# ggsave("04_Output/Plot/EEM/MUS.png")

bl2 <- colorRampPalette(c("#00195911","#001959FF"),alpha=TRUE)(7)

M1 <- matrix(FOR_sd,nrow=length(FOR_eem[[i]]$em),dimnames=list(FOR_eem[[i]]$em,FOR_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q1 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0.1, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank(),plot.subtitle = element_text(hjust = 0.5)) + ylim(250,500) +  coord_flip() +
  theme_minimal(base_size=10) + labs(subtitle="SD EEM") + theme(plot.subtitle = element_text(hjust = 0.5)) +
  scale_fill_manual(values=bl2,drop=FALSE)

M1 <- matrix(SNO_sd,nrow=length(SNO_eem[[i]]$em),dimnames=list(SNO_eem[[i]]$em,SNO_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q2 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +coord_flip() +
  theme_minimal(base_size=10) +
  scale_fill_manual(values=bl2,drop=FALSE)

M1 <- matrix(GLO_sd,nrow=length(GLO_eem[[i]]$em),dimnames=list(GLO_eem[[i]]$em,GLO_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q3 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +coord_flip() +
  theme_minimal(base_size=10) +
  scale_fill_manual(values=bl2,drop=FALSE,name = "SD level (R.U.)")

M1 <- matrix(GRA_sd,nrow=length(GRA_eem[[i]]$em),dimnames=list(GRA_eem[[i]]$em,GRA_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q4 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +coord_flip() +
  theme_minimal(base_size=10) +
  scale_fill_manual(values=bl2,drop=FALSE)

M1 <- matrix(SKY_sd,nrow=length(SKY_eem[[i]]$em),dimnames=list(SKY_eem[[i]]$em,SKY_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q5 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +coord_flip() +
  theme_minimal(base_size=10) +
  scale_fill_manual(values=bl2,drop=FALSE)

M1 <- matrix(PLA_sd,nrow=length(PLA_eem[[i]]$em),dimnames=list(PLA_eem[[i]]$em,PLA_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q6 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  theme(panel.background = element_blank()) + ylim(250,500) +coord_flip() +
  theme_minimal(base_size=10) +
  scale_fill_manual(values=bl2,drop=FALSE)

M1 <- matrix(MUS_sd,nrow=length(MUS_eem[[i]]$em),dimnames=list(MUS_eem[[i]]$em,MUS_eem[[i]]$ex))
M2 <- as.data.frame(as.table(M1))
M2$Ex <- as.numeric(as.character(M2$Var2))
M2$Em <- as.numeric(as.character(M2$Var1))
M2$Freq[length(M2$Freq)] <- 0.20
q7 <- ggplot(M2, aes(Em, Ex, z = Freq)) + geom_contour_filled(size = 0, breaks = c(-0.1,0.01,0.02,0.03,0.05,0.1,0.2,0.4)) + 
  ylim(250,500) +coord_flip() +
  theme_minimal(base_size=10) + theme(panel.background = element_blank(),axis.text.x=element_text(angle=60, hjust=1)) +
  scale_fill_manual(values=bl2,drop=FALSE)



### Compute Plot A
library(ggpubr)  
library(cowplot)
library(tidyr)


library(patchwork)

#get colors as discrete values
colores <- scico(7,palette="romaO",begin = 0.1,
                 end = 0.9)

LegendList <- list(p1 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks")+
                     theme(legend.position="none"),
                   q1 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks") +
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks")+
                     theme(legend.position="none"),
                   p2 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks")+
                     theme(legend.position="none"),
                   q2 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks") +
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks")+
                     theme(legend.position="none"),
                   p3 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks")
                   ,
                   q3 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks") +
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks"),
                   p4 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks")+
                     theme(legend.position="none"),
                   q4 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks") +
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks")+
                     theme(legend.position="none"),
                   p6 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks")+
                     theme(legend.position="none"),
                   q6 + rremove("xlab")+ rremove("x.text")+ rremove("x.ticks") +
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks")+
                     theme(legend.position="none"),
                   p5+theme(legend.position="none") +
                     rremove("xlab")+ rremove("x.text")+ rremove("x.ticks"),
                   q5+theme(legend.position="none")+
                     rremove("xlab")+ rremove("x.text")+ rremove("x.ticks")+
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks"),
                   p7 + 
                     theme(legend.position="none"),
                   q7  +
                     rremove("ylab")+ rremove("y.text")+ rremove("y.ticks")+
                     theme(legend.position="none")
)

#Plot

APlot <- wrap_plots(LegendList,ncol = 2,nrow = 7)
#LegendPlot <- wrap_plots(LegendList,ncol = 2,nrow = 5)
APlot + plot_layout(guides = "collect") + 
  theme(legend.justification = "top")

#ggsave(filename="Fig5A.tiff",width=120,height=220,units='mm',dpi=600)


## B: stacked barplot from each location
#load data
library(readxl)
df_climate2 <- readRDS(file="Data/LaVallina_Fluo.rds")

# Plot
colores <- scico(5,palette="tofino",begin = 0.1,
                 end = 0.9)

lookup <- c(C1="Comp.1",C2="Comp.2",C3="Comp.3",C4="Comp.4",C5="Comp.5")
plotter <- df_climate2 %>% mutate(loc = fct_relevel(loc, 
                                               "FOR","SNO", "GLO","GRA","PLA","SKY","MUS")) %>% 
  select(loc,Comp.4,Comp.2,Comp.3,Comp.5,Comp.1) %>% rename(all_of(lookup)) %>%  
  aggregate(. ~ loc,mean) %>% gather("comp","value",-loc) 
  
plotter$comp <- as.factor(plotter$comp)  

plotter %>% mutate(comp = fct_relevel(comp,"C4","C2","C3","C5","C1")) %>%  
  ggplot(aes(x=loc,y=value,fill=comp)) + xlab("Sampling location") + ylab("stacked fluorescence intensity")+labs(fill="PARAFAC \ncomponent")+
  geom_bar(position="stack", stat="identity") +theme_classic2(base_size=8)+
    theme(axis.text.x=element_text(angle=60, hjust=1),legend.key.size = unit(0.5, 'cm'))+
    scale_fill_manual(values=colores,drop=FALSE)

#ggsave(filename="Fig5B.tiff",width=60,height=60,units='mm',dpi=600)

Fig. 6

## Library
library(knitr)
library(staRdom)
library(lubridate)
library(forcats)

df <- readRDS(file="Data/LaVallina_Fluo.rds") 
#df$Al_ppm <- as.numeric(df$Al_ppm)
df$lubridate <- dmy(df$datee)

df_long <- df %>% gather("param","value",-lubridate,-loc)

var2_names <- c(
  Comp.1 = "C1 (\"humic-like\")" ,
  Comp.2 = "C2 (\"tyrosine-like\")" ,
  Comp.3 = "C3 (\"fossil-like\")" ,
  Comp.4 = "C4 (\"protein-like\")" ,
  Comp.5 = "C5 (\"fulvic-like\")" ,
  C_oddmean = "C1+C3+C5" ,
  C_evenmean = "C2+C4" ,
  C1C5ratio = "C1/C5" ,
  C1C3ratio = "C1/C3" ,
  C3C1ratio = "C3/C1" ,
  C3C5ratio = "C3/C5" ,
  sloperatio ="SR",
  bix = "BIX",
  fi = "FI",
  hix = "HIX",
  drip_d13C_DIC = "drip water d13C",
  driprate = "drip rate",
  logdrip = "log(drip rate)",
  a300 = "a300_stardom"
)


variable_labeller <- function(variable,value){
  return(variable_names[value])
}

capitalize <- function(string) {
  substr(string, 1, 1) <- toupper(substr(string, 1, 1))
  string
}

# Vegetation gradient (defines Symbol in Plot)
df_long$loc <- as.factor(df_long$loc)
df_long$veggradient <- 0
df_long[df_long$loc %in% c("FOR","SNO"),]$veggradient <- "F"#Forest  
df_long[df_long$loc %in% c("GLO","GRA"),]$veggradient <- "B" #Bush
df_long[df_long$loc %in% c("PLA","SKY","MUS"),]$veggradient <- "P" #Pasture


BPlot <- df_long %>% 
  filter(param %in% c("Comp.1","Comp.2","Comp.3","Comp.4","Comp.5")) %>%
  mutate(loc = fct_relevel(loc, 
                           "FOR","SNO", "GLO","GRA","PLA","SKY","MUS"),
         param = fct_relevel(param,"Comp.1","Comp.5","Comp.3","Comp.2","Comp.4")) %>%
  ggplot(aes(y=sqrt(as.numeric(value)),x=lubridate))+
      geom_line(mapping=aes(colour=loc)) +
      geom_point(mapping=aes(shape=as.factor(veggradient),colour=loc)) +
      facet_grid(loc ~ param,scales = "free",labeller=labeller(.default=capitalize,
                                                           param=var2_names),switch="y") +
      coord_cartesian(ylim = c(-0, 0.85)) +
      theme_minimal() +
      theme(axis.text.x=element_text(angle=60, hjust=1),legend.position="none") +
      labs(x = "Date", y = NULL) + scale_x_date(date_labels = "%b %y") +
      scale_y_continuous(position="right") + scale_color_scico_d(palette="romaO",begin = 0.1,
                      end = 0.9)

BPlot

#ggsave(filename="04_Output/Figures_Paper/Endres_Fig7.tiff",width=190,height=140,units='mm',dpi=600)
#A4: 297 mm x 210 mm

Fig. 7

NA

Fig. 8

## Load Library

library(ggplot2)
library(lubridate)
library(tidyverse)
library(ggpubr)
library(tidypaleo)
library(tidyr)
library(dplyr)
library(cowplot)

#library(ggbiplot)
library(factoextra)
library(tinytex)
library(readxl)
library(scico)
library(latex2exp) 

#load data
df_climate2 <- readRDS(file="Data/LaVallina_Fluo.rds") 
df_climate2$loc <- as.factor(df_climate2$loc)


# Plot A
p1 <- df_climate2 %>% mutate(loc = fct_relevel(loc, 
                                                     "FOR","SNO", "GLO","GRA","PLA","SKY","MUS")) %>%  
  ggplot(aes(x=as.factor(thickness),y=Comp.1+Comp.5,colour=loc))+
  geom_boxplot() + theme_classic2() +labs(x="Cover thickness [m]",y="C1+C5")+
  scale_color_scico_d(palette="romaO",begin = 0.1,
                      end = 0.9)

#Plot B
p2 <- df_climate2 %>% mutate(loc = fct_relevel(loc, 
                                                      "FOR","SNO", "GLO","GRA","PLA","SKY","MUS")) %>%
  filter(loc %in% c("FOR","GLO","GRA","SKY","PLA")) %>%
  ggplot(aes(x=as.numeric(d18O_locvar),y=Comp.1+Comp.5,colour=loc))+
  geom_boxplot() + theme_minimal(base_size = 10) +labs(x=TeX("$\\delta ^{18}O$ standard deviation"),y="C1+C5",colour="drip site") +
  scale_color_scico_d(palette="romaO",begin = 0.1,
                      end = 0.9,drop=FALSE)+ theme_classic2()

#Plot C
p3 <- df_climate2 %>% mutate(loc = fct_relevel(loc, 
                                                      "FOR","SNO", "GLO","GRA","PLA","SKY","MUS")) %>%
  filter(loc %in% c("FOR","GLO","GRA","SKY","PLA")) %>%
  ggplot(aes(x=as.numeric(d18O_locvar),y=C3_C1C5ratio,colour=loc))+
  geom_boxplot() + theme_minimal(base_size = 10) +labs(x=TeX("$\\delta ^{18}O$ standard deviation"),y="C3:(C1+C5) ratio",colour="drip site") +
  scale_color_scico_d(palette="romaO",begin = 0.1,
                      end = 0.9,drop=FALSE)+ theme_classic2()



library(patchwork)
wrap_plots(p1+theme(legend.position="none",axis.text.x=element_text(angle=60, hjust=1)),
           p2 +theme(axis.text.x=element_text(angle=60, hjust=1)),
           p3 +theme(axis.text.x=element_text(angle=60, hjust=1)),
           guides="collect") + plot_annotation(tag_levels = 'A')

#ggsave(filename="Endres_Fig8.tiff",width=200*1.3,height=80*1.3,units='mm',dpi=600)